Model training method and device, and method and device for realizing text processing

Abstract

The invention discloses a model training method and device and a method and device for realizing text processing. The method comprises the following steps: expressing a sample text as a sample dependency relationship code according to dependency relationship information; taking the feature vectors of the words contained in the sample text as nodes, and expressing the sample text as a sample matrix; training the sample matrix and the adjacent matrix thereof by initializing a graph neural network model of the operating parameters to obtain a dependency word vector and a subordinate word vector in the sample dependency relationship code; taking the positive sample and the configured negative sample as the input of the graph neural network model, carrying out the training through employing a back propagation method, and determining an operation parameter matrix of the graph neural network model; and processing the input to-be-processed text according to the graph neural network model for determining the operation parameter matrix. According to the embodiment of the invention, the dependency relationship information is added into the model for processing the text, so that the feature representation efficiency of the text content is improved.

Description

technical field [0001] This article involves but is not limited to information processing technology, especially a model training method, device, and text processing method and device. Background technique [0002] The most important step in natural language processing is to represent the text as a feature vector, and perform tasks such as text similarity and classification through the feature vector; the feature vector of the text is obtained by the semantic conversion of the words contained in the text, and the natural language is organized by the grammar. , text analysis through feature vectors will lose part of the information in the grammar; some text analysis models will add the position information of words to express the relationship between words, but the relationship between words will still be lost. How to represent text content more effectively has become an unresolved problem. Contents of the invention [0003] The following is an overview of the topics descr...

AI Technical Summary

Problems solved by technology

[0002] The most important step in natural language processing is to represent the text as a feature vector, and perform tasks such as text similarity and classification through the feature vector; the feature vector of the text is obtained by the semantic conversion of the words contained in the text, and the natural language is organized by the grammar. , text analysis through feature vectors will lose part of the information in the grammar; some text analysis models will add word position information to express the relationship between words, but the relationship between words will still be lost

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